4.7 Article

Genomic Profiling of Smoldering Multiple Myeloma Identifies Patients at a High Risk of Disease Progression

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JOURNAL OF CLINICAL ONCOLOGY
卷 38, 期 21, 页码 2380-+

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AMER SOC CLINICAL ONCOLOGY
DOI: 10.1200/JCO.20.00437

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  1. National Institutes of Health [R01 CA 205954]
  2. Multiple Myeloma Research Foundation-Perelman Prevention Program grant
  3. Leukemia and Lymphoma Society Specialized Center of Research grant
  4. Stand Up To Cancer Dream Team grant
  5. Adelson Medical Research Foundation
  6. Cancer Research UK Early Detection Program grant

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PURPOSESmoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with a 10% annual risk of progression. Various prognostic models exist for risk stratification; however, those are based on solely clinical metrics. The discovery of genomic alterations that underlie disease progression to MM could improve current risk models.METHODSWe used next-generation sequencing to study 214 patients with SMM. We performed whole-exome sequencing on 166 tumors, including 5 with serial samples, and deep targeted sequencing on 48 tumors.RESULTSWe observed that most of the genetic alterations necessary for progression have already been acquired by the diagnosis of SMM. Particularly, we found that alterations of the mitogen-activated protein kinase pathway (KRAS and NRAS single nucleotide variants [SNVs]), the DNA repair pathway (deletion 17p, TP53, and ATM SNVs), and MYC (translocations or copy number variations) were all independent risk factors of progression after accounting for clinical risk staging. We validated these findings in an external SMM cohort by showing that patients who have any of these three features have a higher risk of progressing to MM. Moreover, APOBEC associated mutations were enriched in patients who progressed and were associated with a shorter time to progression in our cohort.CONCLUSIONSMM is a genetically mature entity whereby most driver genetic alterations have already occurred, which suggests the existence of a right-skewed model of genetic evolution from monoclonal gammopathy of undetermined significance to MM. We identified and externally validated genomic predictors of progression that could distinguish patients at high risk of progression to MM and, thus, improve on the precision of current clinical models.

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